{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:32:44.347663Z",
     "start_time": "2025-07-04T07:32:42.726102Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 将trade_date字段转成日期格式，并设置为索引字段\n",
    "df = pd.read_csv('600030.csv', parse_dates=['trade_date'], index_col='trade_date')"
   ],
   "id": "884435c3dd841e63",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:37:26.173144Z",
     "start_time": "2025-07-04T07:37:26.167926Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df.sort_index(ascending=True, inplace=True)\n",
    "data = df['close']\n",
    "data"
   ],
   "id": "b992f51263b3de8b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2018-01-02    18.44\n",
       "2018-01-03    18.61\n",
       "2018-01-04    18.67\n",
       "2018-01-05    18.88\n",
       "2018-01-08    19.54\n",
       "              ...  \n",
       "2019-12-25    23.13\n",
       "2019-12-26    23.75\n",
       "2019-12-27    23.33\n",
       "2019-12-30    25.66\n",
       "2019-12-31    25.30\n",
       "Name: close, Length: 476, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:37:41.583580Z",
     "start_time": "2025-07-04T07:37:41.497326Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 绘制收盘价\n",
    "data.plot()"
   ],
   "id": "7999dd49be1a0fe2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trade_date'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:37:46.371686Z",
     "start_time": "2025-07-04T07:37:46.366541Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练集\n",
    "train_data = data['2018-01-02':'2019-10-08']\n",
    "train_data[:10]"
   ],
   "id": "feed1b1cca2f7c83",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2018-01-02    18.44\n",
       "2018-01-03    18.61\n",
       "2018-01-04    18.67\n",
       "2018-01-05    18.88\n",
       "2018-01-08    19.54\n",
       "2018-01-09    19.44\n",
       "2018-01-10    19.61\n",
       "2018-01-11    19.28\n",
       "2018-01-12    19.33\n",
       "2018-01-15    19.45\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:37:50.143754Z",
     "start_time": "2025-07-04T07:37:50.137670Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最后3个月作为测试集\n",
    "test_data = data['2019-10-08':]\n",
    "test_data[:10]"
   ],
   "id": "7a26857f7bf4d0fa",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2019-10-08    22.33\n",
       "2019-10-09    22.23\n",
       "2019-10-10    22.39\n",
       "2019-10-11    22.94\n",
       "2019-10-14    23.09\n",
       "2019-10-15    22.69\n",
       "2019-10-16    22.56\n",
       "2019-10-17    22.52\n",
       "2019-10-18    22.00\n",
       "2019-10-21    21.85\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:45:08.304662Z",
     "start_time": "2025-07-04T07:45:08.295214Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch\n",
    "\n",
    "# 超参数设置\n",
    "SEQ_LENGTH = 3  # 输入序列长度\n",
    "BATCH_SIZE = 1\n",
    "HIDDEN_SIZE = 128\n",
    "\n",
    "\n",
    "# 创建训练数据\n",
    "class ZhouyuDataset(Dataset):\n",
    "    def __init__(self, data, seq_length):\n",
    "        self.seq_length = seq_length\n",
    "\n",
    "        # 统一做归一化\n",
    "        self.data = (data.values - data.values.min()) / (data.values.max() - data.values.min())\n",
    "\n",
    "        # 分别对inputs、targets做归一化\n",
    "        # 和统一做归一化差不太多\n",
    "        # 比如[0,1,2,3,4,5,6,7,8,9]，最大值是9，最小值是0\n",
    "        # inputs = [[0,1,2],[1,2,3]...[6,7,8]]，比全部数据少个数，最大值是8，最小值是0\n",
    "        # targets = [3,4,5,6,7,8,9]，比全部数据少3个数，最大值是9，最小值是3\n",
    "        # inputs, targets = [], []\n",
    "        # for i in range(len(data.values) - seq_length):\n",
    "        #     seq = data.values[i:i + seq_length]\n",
    "        #     target = data.values[i + seq_length]\n",
    "        #     inputs.append(seq)\n",
    "        #     targets.append(target)\n",
    "        # inputs = np.array(inputs)\n",
    "        # targets = np.array(targets)\n",
    "        # self.inputs_min = inputs.min()\n",
    "        # self.inputs_max = inputs.max()\n",
    "        # self.targets_min = targets.min()\n",
    "        # self.targets_max = targets.max()\n",
    "        # self.inputs = (inputs - self.inputs_min) / (self.inputs_max - self.inputs_min)\n",
    "        # self.targets = (targets - self.targets_min) / (self.targets_max - self.targets_min)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data) - self.seq_length\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        inputs = self.data[idx:idx + self.seq_length]\n",
    "        targets = self.data[idx + self.seq_length]\n",
    "        return torch.tensor(inputs, dtype=torch.float32), torch.tensor(targets, dtype=torch.float32)\n",
    "        # return torch.tensor(self.inputs[idx], dtype=torch.float32), torch.tensor(self.targets[idx], dtype=torch.float32)\n",
    "\n",
    "\n",
    "train_dataset = ZhouyuDataset(train_data, SEQ_LENGTH)\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "print(train_dataset.data)"
   ],
   "id": "c2154c2415651fd4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.30201342 0.31627517 0.32130872 0.33892617 0.3942953  0.38590604\n",
      " 0.40016779 0.37248322 0.37667785 0.38674497 0.45385906 0.51174497\n",
      " 0.5511745  0.54110738 0.53355705 0.53439597 0.67785235 0.6283557\n",
      " 0.62919463 0.60989933 0.56963087 0.54110738 0.55872483 0.56040268\n",
      " 0.58305369 0.47902685 0.39513423 0.36912752 0.21057047 0.20889262\n",
      " 0.23322148 0.2340604  0.2659396  0.28104027 0.31963087 0.28607383\n",
      " 0.27432886 0.27600671 0.25083893 0.25419463 0.30033557 0.28775168\n",
      " 0.29781879 0.33724832 0.37416107 0.34479866 0.3238255  0.33221477\n",
      " 0.29362416 0.31040268 0.32214765 0.30704698 0.32885906 0.23993289\n",
      " 0.26677852 0.29194631 0.2533557  0.29110738 0.31375839 0.34312081\n",
      " 0.36661074 0.34983221 0.37751678 0.41107383 0.38674497 0.35822148\n",
      " 0.34228188 0.29781879 0.29949664 0.33137584 0.34983221 0.2885906\n",
      " 0.30956376 0.36912752 0.36073826 0.32718121 0.34815436 0.36073826\n",
      " 0.39848993 0.38003356 0.40184564 0.42785235 0.41191275 0.41946309\n",
      " 0.39010067 0.42197987 0.41778523 0.39261745 0.36996644 0.4102349\n",
      " 0.41694631 0.40771812 0.39177852 0.38003356 0.35402685 0.36996644\n",
      " 0.33808725 0.27516779 0.31879195 0.31291946 0.32298658 0.32969799\n",
      " 0.3238255  0.34899329 0.31459732 0.3238255  0.33389262 0.31124161\n",
      " 0.32130872 0.3011745  0.20218121 0.18791946 0.12080537 0.14261745\n",
      " 0.11073826 0.12751678 0.10067114 0.11073826 0.14513423 0.10067114\n",
      " 0.12416107 0.10486577 0.09899329 0.11157718 0.14010067 0.1409396\n",
      " 0.10402685 0.14597315 0.14765101 0.1409396  0.125      0.11325503\n",
      " 0.11828859 0.16694631 0.19295302 0.20805369 0.20385906 0.17449664\n",
      " 0.16778523 0.17869128 0.16946309 0.1442953  0.10738255 0.09899329\n",
      " 0.0897651  0.12919463 0.09983221 0.12919463 0.1283557  0.11493289\n",
      " 0.11325503 0.0864094  0.09312081 0.07634228 0.10318792 0.11073826\n",
      " 0.11157718 0.12080537 0.09731544 0.125      0.11493289 0.10318792\n",
      " 0.09228188 0.09563758 0.10486577 0.11996644 0.09060403 0.08557047\n",
      " 0.08808725 0.04865772 0.05704698 0.05704698 0.07298658 0.0704698\n",
      " 0.06291946 0.08892617 0.10822148 0.1090604  0.15436242 0.13171141\n",
      " 0.1442953  0.14177852 0.15520134 0.09395973 0.10067114 0.10989933\n",
      " 0.02768456 0.03104027 0.01510067 0.00503356 0.02097315 0.\n",
      " 0.04530201 0.17449664 0.15687919 0.17533557 0.21057047 0.17114094\n",
      " 0.15184564 0.20637584 0.19463087 0.2114094  0.24748322 0.24077181\n",
      " 0.24077181 0.20637584 0.17869128 0.17449664 0.19211409 0.21392617\n",
      " 0.18540268 0.2147651  0.22986577 0.27097315 0.21224832 0.19295302\n",
      " 0.18456376 0.14765101 0.14513423 0.15687919 0.18204698 0.15016779\n",
      " 0.17030201 0.21812081 0.23154362 0.21895973 0.18204698 0.18540268\n",
      " 0.16191275 0.17114094 0.17869128 0.19630872 0.16862416 0.1795302\n",
      " 0.15184564 0.13255034 0.13926174 0.1057047  0.09815436 0.16442953\n",
      " 0.17869128 0.18120805 0.23154362 0.23489933 0.22818792 0.25503356\n",
      " 0.25167785 0.22063758 0.22483221 0.25587248 0.26845638 0.26510067\n",
      " 0.27348993 0.26006711 0.28691275 0.31375839 0.33724832 0.34647651\n",
      " 0.39010067 0.37416107 0.31879195 0.41778523 0.41442953 0.43959732\n",
      " 0.46560403 0.63674497 0.82466443 0.81040268 0.77265101 0.72734899\n",
      " 0.80704698 0.80956376 0.83557047 0.99496644 1.         0.77516779\n",
      " 0.77600671 0.82550336 0.76174497 0.71560403 0.75251678 0.82130872\n",
      " 0.78775168 0.81543624 0.84395973 0.83557047 0.74328859 0.67701342\n",
      " 0.68204698 0.67030201 0.83389262 0.87583893 0.86912752 0.94966443\n",
      " 0.93624161 0.8783557  0.88674497 0.84815436 0.80369128 0.79110738\n",
      " 0.75167785 0.81040268 0.79865772 0.79194631 0.81040268 0.75083893\n",
      " 0.76426174 0.78355705 0.72651007 0.7340604  0.68624161 0.68791946\n",
      " 0.49496644 0.49580537 0.45553691 0.41442953 0.48657718 0.43624161\n",
      " 0.42281879 0.45805369 0.45218121 0.40268456 0.42197987 0.45218121\n",
      " 0.44463087 0.43708054 0.43875839 0.48573826 0.48238255 0.47902685\n",
      " 0.45050336 0.44379195 0.4488255  0.44379195 0.46057047 0.43875839\n",
      " 0.43959732 0.52432886 0.50083893 0.51761745 0.48825503 0.49412752\n",
      " 0.50419463 0.56291946 0.68875839 0.74077181 0.73489933 0.72483221\n",
      " 0.70889262 0.76174497 0.75251678 0.80033557 0.78104027 0.72063758\n",
      " 0.73489933 0.72986577 0.6988255  0.70385906 0.70134228 0.69966443\n",
      " 0.71560403 0.74244966 0.72063758 0.69211409 0.66107383 0.69966443\n",
      " 0.66778523 0.67365772 0.70637584 0.71728188 0.71728188 0.6988255\n",
      " 0.71895973 0.70385906 0.67785235 0.57634228 0.51174497 0.50419463\n",
      " 0.47483221 0.50922819 0.49916107 0.54530201 0.52265101 0.52936242\n",
      " 0.53355705 0.54446309 0.6954698  0.66442953 0.65436242 0.6602349\n",
      " 0.65436242 0.61912752 0.66526846 0.63590604 0.6216443  0.62751678\n",
      " 0.66526846 0.67114094 0.71224832 0.75419463 0.78104027 0.79530201\n",
      " 0.7852349  0.8011745  0.82802013 0.79194631 0.72902685 0.72063758\n",
      " 0.7340604  0.71308725 0.68204698 0.68120805 0.67281879 0.66107383\n",
      " 0.68875839 0.6409396  0.6283557 ]\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:45:10.414518Z",
     "start_time": "2025-07-04T07:45:10.409039Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for input_seq, target_seq in train_dataloader:\n",
    "    print(input_seq)\n",
    "    print(target_seq)\n",
    "    break"
   ],
   "id": "99cb25c74d473da8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.3020, 0.3163, 0.3213]])\n",
      "tensor([0.3389])\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:32:46.443526Z",
     "start_time": "2025-07-04T07:32:45.823845Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch import nn\n",
    "\n",
    "\n",
    "# 大都督周瑜（我的微信: dadudu6789）\n",
    "class ZhouyuNet(nn.Module):\n",
    "    def __init__(self, hidden_size: int):\n",
    "        super().__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        # 输入给RNN的某天的股价\n",
    "        self.rnn = nn.RNN(1, hidden_size, batch_first=True)\n",
    "        self.out_linear = nn.Linear(hidden_size, 1)  # 输出后一天的股价\n",
    "\n",
    "    def forward(self, x, hidden=None):\n",
    "        outputs, hidden = self.rnn(x, hidden)  # outputs的形状为(batch_size, seq_len, hidden_size)\n",
    "        out = self.out_linear(hidden)\n",
    "        return out, hidden\n",
    "\n",
    "\n",
    "# 初始化模型\n",
    "model = ZhouyuNet(HIDDEN_SIZE)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.001)"
   ],
   "id": "d7bc18eb9cfc976f",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:49:18.168999Z",
     "start_time": "2025-07-04T07:49:08.912126Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "# 开始训练\n",
    "writer = SummaryWriter()\n",
    "\n",
    "for epoch in range(100):\n",
    "    for i, (inputs, targets) in enumerate(train_dataloader):\n",
    "        inputs = inputs.view(inputs.size(0), inputs.size(1), 1)\n",
    "        outputs, _ = model(inputs)\n",
    "        loss = criterion(outputs.view(-1), targets)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if (i + 1) % 100 == 0:\n",
    "            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
    "                  .format(epoch + 1, 100, i + 1, len(train_dataloader), loss.item()))\n",
    "\n",
    "            global_step = epoch * len(train_dataloader) + i\n",
    "            writer.add_scalar('training loss', loss, global_step)"
   ],
   "id": "b3ab92e81e89a1b1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [1/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [1/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [1/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [2/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [2/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [2/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [2/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [3/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [3/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [3/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [3/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [4/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [4/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [4/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [4/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [5/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [5/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [5/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [5/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [6/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [6/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [6/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [6/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [7/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [7/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [7/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [7/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [8/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [8/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [8/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [8/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [9/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [9/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [9/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [9/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [10/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [10/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [10/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [10/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [11/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [11/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [11/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [11/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [12/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [12/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [12/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [12/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [13/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [13/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [13/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [13/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [14/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [14/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [14/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [14/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [15/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [15/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [15/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [15/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [16/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [16/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [16/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [16/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [17/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [17/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [17/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [17/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [18/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [18/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [18/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [18/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [19/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [19/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [19/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [19/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [20/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [20/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [20/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [20/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [21/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [21/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [21/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [21/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [22/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [22/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [22/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [22/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [23/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [23/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [23/100], Step [300/414], Loss: 0.0003\n",
      "Epoch [23/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [24/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [24/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [24/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [24/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [25/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [25/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [25/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [25/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [26/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [26/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [26/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [26/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [27/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [27/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [27/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [27/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [28/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [28/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [28/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [28/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [29/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [29/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [29/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [29/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [30/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [30/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [30/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [30/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [31/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [31/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [31/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [31/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [32/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [32/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [32/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [32/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [33/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [33/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [33/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [33/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [34/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [34/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [34/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [34/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [35/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [35/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [35/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [35/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [36/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [36/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [36/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [36/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [37/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [37/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [37/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [37/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [38/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [38/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [38/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [38/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [39/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [39/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [39/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [39/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [40/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [40/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [40/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [40/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [41/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [41/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [41/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [41/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [42/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [42/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [42/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [42/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [43/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [43/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [43/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [43/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [44/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [44/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [44/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [44/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [45/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [45/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [45/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [45/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [46/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [46/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [46/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [46/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [47/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [47/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [47/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [47/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [48/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [48/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [48/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [48/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [49/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [49/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [49/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [49/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [50/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [50/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [50/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [50/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [51/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [51/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [51/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [51/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [52/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [52/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [52/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [52/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [53/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [53/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [53/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [53/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [54/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [54/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [54/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [54/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [55/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [55/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [55/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [55/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [56/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [56/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [56/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [56/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [57/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [57/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [57/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [57/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [58/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [58/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [58/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [58/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [59/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [59/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [59/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [59/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [60/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [60/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [60/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [60/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [61/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [61/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [61/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [61/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [62/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [62/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [62/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [62/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [63/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [63/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [63/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [63/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [64/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [64/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [64/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [64/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [65/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [65/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [65/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [65/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [66/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [66/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [66/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [66/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [67/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [67/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [67/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [67/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [68/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [68/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [68/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [68/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [69/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [69/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [69/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [69/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [70/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [70/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [70/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [70/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [71/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [71/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [71/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [71/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [72/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [72/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [72/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [72/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [73/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [73/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [73/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [73/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [74/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [74/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [74/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [74/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [75/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [75/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [75/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [75/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [76/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [76/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [76/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [76/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [77/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [77/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [77/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [77/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [78/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [78/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [78/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [78/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [79/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [79/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [79/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [79/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [80/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [80/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [80/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [80/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [81/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [81/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [81/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [81/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [82/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [82/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [82/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [82/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [83/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [83/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [83/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [83/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [84/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [84/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [84/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [84/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [85/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [85/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [85/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [85/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [86/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [86/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [86/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [86/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [87/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [87/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [87/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [87/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [88/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [88/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [88/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [88/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [89/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [89/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [89/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [89/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [90/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [90/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [90/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [90/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [91/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [91/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [91/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [91/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [92/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [92/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [92/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [92/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [93/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [93/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [93/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [93/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [94/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [94/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [94/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [94/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [95/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [95/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [95/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [95/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [96/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [96/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [96/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [96/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [97/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [97/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [97/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [97/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [98/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [98/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [98/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [98/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [99/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [99/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [99/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [99/100], Step [400/414], Loss: 0.0000\n",
      "Epoch [100/100], Step [100/414], Loss: 0.0000\n",
      "Epoch [100/100], Step [200/414], Loss: 0.0017\n",
      "Epoch [100/100], Step [300/414], Loss: 0.0002\n",
      "Epoch [100/100], Step [400/414], Loss: 0.0000\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:50:16.716681Z",
     "start_time": "2025-07-04T07:50:16.713471Z"
    }
   },
   "cell_type": "code",
   "source": "train_dataset[0][0]",
   "id": "6c375a64080e0e2d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.3020, 0.3163, 0.3213])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:51:10.127881Z",
     "start_time": "2025-07-04T07:51:10.122922Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 手动预测\n",
    "pred_normalized = model(train_dataset[0][0].view(1, -1, 1))[0]\n",
    "pred = pred_normalized * (train_data.values.max() - train_data.values.min()) + train_data.values.min()\n",
    "# pred = pred_normalized * (train_dataset.targets_max - train_dataset.targets_min) + train_dataset.targets_min\n",
    "# pred = pred_normalized\n",
    "print(pred)"
   ],
   "id": "281d1e1df85924f5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[18.7833]]], grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:52:21.602747Z",
     "start_time": "2025-07-04T07:52:21.525460Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 画出预测结果和真实结果的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model.eval()\n",
    "\n",
    "train_pred_result = []\n",
    "with torch.no_grad():\n",
    "    for inputs, targets in train_dataset:\n",
    "        train_pred = model(inputs.view(1, -1, 1))\n",
    "        pred_normalized = train_pred[0][0][0]\n",
    "        pred = pred_normalized * (train_data.values.max() - train_data.values.min()) + train_data.values.min()\n",
    "        train_pred_result.append(pred)\n",
    "\n",
    "train_pred_result = [train_pred_result[0]] * SEQ_LENGTH + train_pred_result\n",
    "\n",
    "print(len(train_pred_result))\n",
    "print(len(train_data.values))\n",
    "\n",
    "x_plot = [i for i in range(len(train_pred_result))]\n",
    "\n",
    "plt.plot(x_plot, train_data.values, label='real')\n",
    "plt.plot(x_plot, train_pred_result, label='pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "af1aa0c9ae3860d8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "417\n",
      "417\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:32:56.057028Z",
     "start_time": "2025-07-04T07:32:56.053833Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([[1, 2], [3, 4]])\n",
    "a - a.min()"
   ],
   "id": "d6838e4ffb0c436a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:52:39.620580Z",
     "start_time": "2025-07-04T07:52:39.613972Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch\n",
    "\n",
    "# 超参数设置\n",
    "SEQ_LENGTH = 3  # 输入序列长度\n",
    "\n",
    "\n",
    "# 创建训练数据\n",
    "class ZhouyuTestDataset(Dataset):\n",
    "    def __init__(self, data, seq_length):\n",
    "        self.seq_length = seq_length\n",
    "        # 用训练集最大最小值统一做归一化\n",
    "        self.data = (data.values - train_data.values.min()) / (train_data.values.max() - train_data.values.min())\n",
    "    def __len__(self):\n",
    "        return len(self.data) - self.seq_length\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        inputs = self.data[idx:idx + self.seq_length]\n",
    "        targets = self.data[idx + self.seq_length]\n",
    "        return torch.tensor(inputs, dtype=torch.float32), torch.tensor(targets, dtype=torch.float32)\n",
    "\n",
    "\n",
    "test_dataset = ZhouyuTestDataset(test_data, SEQ_LENGTH)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)\n",
    "print(test_dataset.data)"
   ],
   "id": "b2df8a4103011a4e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6283557  0.61996644 0.63338926 0.6795302  0.69211409 0.65855705\n",
      " 0.64765101 0.6442953  0.60067114 0.58808725 0.6090604  0.58305369\n",
      " 0.58808725 0.6057047  0.63003356 0.5897651  0.57550336 0.61577181\n",
      " 0.62248322 0.65268456 0.63674497 0.6602349  0.63590604 0.60067114\n",
      " 0.60318792 0.58724832 0.59228188 0.57634228 0.59144295 0.61325503\n",
      " 0.57718121 0.5704698  0.55788591 0.56879195 0.56040268 0.55285235\n",
      " 0.55956376 0.5511745  0.5738255  0.58808725 0.58137584 0.62583893\n",
      " 0.63255034 0.6216443  0.61828859 0.61996644 0.61744966 0.68456376\n",
      " 0.7114094  0.75755034 0.75167785 0.74748322 0.73322148 0.68875839\n",
      " 0.69966443 0.6954698  0.74748322 0.71224832 0.90771812 0.87751678]\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-04T07:53:08.779140Z",
     "start_time": "2025-07-04T07:53:08.723410Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 画出预测结果和真实结果的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model.eval()\n",
    "\n",
    "test_pred_result = []\n",
    "targets_result = []\n",
    "with torch.no_grad():\n",
    "    for inputs, targets in test_dataset:\n",
    "        test_pred = model(inputs.view(1, -1, 1))\n",
    "        pred_normalized = test_pred[0][0][0]\n",
    "        pred = pred_normalized * (train_data.values.max() - train_data.values.min()) + train_data.values.min()\n",
    "        test_pred_result.append(pred)\n",
    "\n",
    "        targets = targets * (train_data.values.max() - train_data.values.min()) + train_data.values.min()\n",
    "        targets_result.append(targets)\n",
    "\n",
    "x_plot = [i for i in range(len(targets_result))]\n",
    "\n",
    "plt.plot(x_plot, targets_result, label='real')\n",
    "plt.plot(x_plot, test_pred_result, label='pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "d412f462b112034c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 30
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
